A Real-time Robust Approach for Tracking UAVs in Infrared Videos

Wu, Han; Li, Weiqiang; Li, Wanqi; Liu, Guizhong

doi:10.1109/cvprw50498.2020.00524

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…However, tracking UAVs in infrared video is still a challenging task due to the complexity and variability of practical application scenarios. In the case where the target disappears during the tracking process, H. Wu 27 tracks the target by searching a larger adjacent region, while J. Zhao 28 and Q. Yu 29 use the global detection module to re-detect the target. For the case where the target features are not obvious, H. Fang 1 and H. Wu 27 use the channel attention mechanism to enhance the target features.…”

Section: Thermal Infrared Uav Trackingmentioning

confidence: 99%

“…In the case where the target disappears during the tracking process, H. Wu 27 tracks the target by searching a larger adjacent region, while J. Zhao 28 and Q. Yu 29 use the global detection module to re-detect the target. For the case where the target features are not obvious, H. Fang 1 and H. Wu 27 use the channel attention mechanism to enhance the target features. Considering the camera motion, J. Zhao 28 and Z. Wang 30 extract key points and calculate the homography matrix to compensate for the motion between frames.…”

Section: Thermal Infrared Uav Trackingmentioning

confidence: 99%

See 1 more Smart Citation

A real-time global-search method for infrared UAV tracking

Feng,

Sun,

Zhang

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Tracking unmanned aerial vehicles (UAVs) using infrared cameras is an important technology for the anti-UAV task. Most of the existing trackers adopt the local-search method that crops out an image patch for tracking targets, which is not effective in dealing with challenging situations such as target disappearance and tracking failure. And attaching a global detection module to the local tracker or directly using a global tracker will require too much computation and cannot meet the real time requirement. Therefore, we propose a real-time global-search method for tracking UAVs in infrared videos, dubbed RGT, which is based on a one-stage anchor-free deep framework and introduces an attention module to enhance the ability to discriminate between the target and the background. In addition, according to the multiscale output characteristics in our model, we propose an adaptive multi-scale template updating (AMTU) and a multiscale spatial constraint (MSC) to address the problems of target scale variation and background clutter, respectively. We use the 1st, 2nd and 3rd Anti-UAV datasets for training and testing, and implement comparison experiments with 10 deep trackers. Our algorithm shows excellent performance and can run in real time at 30FPS on an NVIDIA RTX 2080Ti GPU.

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Section: Thermal Infrared Uav Trackingmentioning

confidence: 99%

Section: Thermal Infrared Uav Trackingmentioning

confidence: 99%

A real-time global-search method for infrared UAV tracking

Feng,

Sun,

Zhang

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

View full text Add to dashboard Cite

show abstract

“…While it excels in small object detection, future research will address further improvements for feature-less objects like bicycles. Wu et al [16] propose a robust and real-time tracking algorithm for infrared drones, incorporating a feature attention module and an expansion strategy for searching the target. The algorithm is designed to track drones in real-time, addressing the challenges of realinfrared scenes with high efficiency.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

Soeleman,

Supriyanto,

Purwanto

2023

RIA

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This research is related to the use of deep learning models on thermal images for object detection using Unmanned Aerial Vehicles (UAVs). Thermal imaging proves to be efficient in environments with minimal light and during nighttime, as it operates based on emitted heat rather than visible light. The ability to detect objects in thermal images enhances surveillance and security measures, particularly in dark conditions. During search and rescue missions, especially in settings with restricted visibility, thermal imaging aids in the identification of individuals or animals by detecting their heat signatures. This facilitates the process of locating them in diverse conditions. Detecting objects at night is challenging due to the lack of illumination. Experiments were conducted using the publicly available HIT-UAV dataset, consisting of 2898 images. This dataset includes several classes such as Person, Car, Bicycle, Other Vehicle, and DontCare. This study proposes the use of both YOLOv5 and YOLOv8 for object detection on this dataset. The YOLOv8 model is the latest model currently available. Both YOLOv5 and YOLOv8 variants were developed by Ultralytics. The experiment used five Yolo models: nano (n), small (s), medium (m), large (l), and extra-large (x). By using YOLOv8m, we achieved a mean Average Precision at IoU threshold 0.5

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“…This deep semantic feature is obtained by a pre-trained VGGNet combined with a target feature channel selection module based on supervised training. Wu et al [ 17 ] proposed a TIR UAV object tracking method based on full convolutional regression network. Zulkifley et al [ 18 ] proposed a TIR object tracking method that combines a binary fully convolutional network with an offline pre-trained Siamese network.…”

Section: Related Workmentioning

confidence: 99%

Thermal Infrared Tracking Method Based on Efficient Global Information Perception

Zhao

Liu

Ren

et al. 2022

Sensors

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To solve the insufficient ability of the current Thermal InfraRed (TIR) tracking methods to resist occlusion and interference from similar targets, we propose a TIR tracking method based on efficient global information perception. In order to efficiently obtain the global semantic information of images, we use the Transformer structure for feature extraction and fusion. In the feature extraction process, the Focal Transformer structure is used to improve the efficiency of remote information modeling, which is highly similar to the human attention mechanism. The feature fusion process supplements the relative position encoding to the standard Transformer structure, which allows the model to continuously consider the influence of positional relationships during the learning process. It can also generalize to capture the different positional information for different input sequences. Thus, it makes the Transformer structure model the semantic information contained in images more efficiently. To further improve the tracking accuracy and robustness, the heterogeneous bi-prediction head is utilized in the object prediction process. The fully connected sub-network is responsible for the classification prediction of the foreground or background. The convolutional sub-network is responsible for the regression prediction of the object bounding box. In order to alleviate the contradiction between the vast demand for training data of the Transformer model and the insufficient scale of the TIR tracking dataset, the LaSOT-TIR dataset is generated with the generative adversarial network for network training. Our method achieves the best performance compared with other state-of-the-art trackers on the VOT2015-TIR, VOT2017-TIR, PTB-TIR and LSOTB-TIR datasets, and performs outstandingly especially when dealing with severe occlusion or interference from similar objects.

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A Real-time Robust Approach for Tracking UAVs in Infrared Videos

Cited by 12 publications

References 25 publications

A real-time global-search method for infrared UAV tracking

A real-time global-search method for infrared UAV tracking

Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

Thermal Infrared Tracking Method Based on Efficient Global Information Perception

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